C2T introduces an innovative classifier-based method for tree construction in speculative decoding, significantly reducing inference latency and computational costs of large language models.

• Uses a lightweight classifier to dynamically predict token probabilities
• Constructs optimized token trees for more efficient verification processes
• Demonstrates superior performance compared to chain mode and static/dynamic tree approaches
• Addresses critical inference bottlenecks as LLMs continue to scale in size

This engineering advancement matters because it makes large language models more practical and cost-effective for real-world applications, enabling faster response times without sacrificing quality.

C2T: A Classifier-Based Tree Construction Method in Speculative Decoding